PPE.

1 👥 Cohort Description

Cohort Overview

Table 1a. Cohort Description: Overall
Characteristic N = 1471
Age
    Median (Q1, Q3) 37 (34, 44)
    Unknown 13
Age group
    age ≥15-<25 1/134 (0.7%)
    age ≥25-<35 45/134 (34%)
    age ≥35-<45 62/134 (46%)
    age ≥45-<55 19/134 (14%)
    age ≥55 7/134 (5.2%)
    Unknown 13
Sex
    Female 38/134 (28%)
    Male 96/134 (72%)
    Unknown 13
Period
    First Three Days 74/147 (50%)
    Last Two Days 73/147 (50%)
Years of Professional Experience
    Median (Q1, Q3) 11 (8, 17)
    Unknown 13
Experience in VHF Response 101/134 (75%)
    Unknown 13
The IDTM is an Advantage to the Systems Used in the Past 117/121 (97%)
    Unknown 26
Envisioning Usage of IDMT in the Future
    Yes 121/121 (100%)
    Unknown 26
1 n/N (%)

Age and Sex Distribution

Cohort by Period

Table 1b. Cohort Description by Period
Characteristic Overall
N = 147
1
First Three Days
N = 74
1
Last Two Days
N = 73
1
p-value
Age
    Median (Q1, Q3) 37 (34, 44) 37 (34, 43) 37 (34, 44)
    Unknown 13 8 5
Age group
    age ≥15-<25 1/134 (0.7%) 1/66 (1.5%) 0/68 (0%)
    age ≥25-<35 45/134 (34%) 22/66 (33%) 23/68 (34%)
    age ≥35-<45 62/134 (46%) 32/66 (48%) 30/68 (44%)
    age ≥45-<55 19/134 (14%) 8/66 (12%) 11/68 (16%)
    age ≥55 7/134 (5.2%) 3/66 (4.5%) 4/68 (5.9%)
    Unknown 13 8 5
Sex
    Female 38/134 (28%) 18/66 (27%) 20/68 (29%)
    Male 96/134 (72%) 48/66 (73%) 48/68 (71%)
    Unknown 13 8 5
Years of Professional Experience
    Median (Q1, Q3) 11 (8, 17) 11 (8, 17) 12 (8, 18)
    Unknown 13 8 5
Experience in VHF Response 101/134 (75%) 50/66 (76%) 51/68 (75%)
    Unknown 13 8 5
The IDTM is an Advantage to the Systems Used in the Past 117/121 (97%) 58/61 (95%) 59/60 (98%)
    Unknown 26 13 13
Envisioning Usage of IDMT in the Future
    Yes 121/121 (100%) 61/61 (100%) 60/60 (100%)
    Unknown 26 13 13
1 n/N (%)

2 📊 Survey Overview

Overall, 96.7% of participants considered IDTM an advantage to the systems used in the past and 100% are envisioning the usage of IDMT in the future (see Cohort Description above).

2.1 UEQ Scales Summary

Category Average1 Std Dev. Std Err. 95% CI 2
Attractiveness 2.337 0.867 0.079 2.18-2.49
Perspicuity 1.853 1.078 0.098 1.66-2.05
Novelty 1.463 1.106 0.101 1.26-1.66
Stimulation 2.324 0.916 0.083 2.16-2.49
Dependability 1.897 0.952 0.087 1.73-2.07
Efficiency 2.132 0.942 0.086 1.96-2.3
1 Values between -0.8and 0.8 represent a more or less neutral evaluation of the corresponding scale, values > 0,8 represent a positive evaluation and values < -0,8 represent a negative evaluation.
2 ▲ Positive Evaluation | ■︎ Neutral | ▼ Negative Evaluation)

2.2 Scales Distribution

Overall

By period

2.3 UEQ Mean values per items

2.4 Benchmark

The benchmark classifies a product into 5 categories (per scale): 

  • Excellent: In the range of the 10% best results. 

  • Good: 10% of the results in the benchmark data set are better and 75% of the results are worse. 

  • Above average: 25% of the results in the benchmark are better than the result for the evaluated product, 50% of the results are worse. 

  • Below average: 50% of the results in the benchmark are better than the result for the evaluated product, 25% of the results are worse. 

  • Bad: In the range of the 25% worst results. 

3 📊 Distribution of Answers per Item

Each item was rated on a 7-point scale from the left concept to the right. Lower values indicate stronger agreement with the first term (e.g., ‘Annoying’), while higher values reflect stronger agreement with the second (e.g., ‘Enjoyable’).

Attractiveness

Perspicuity

Novelty

Stimulation

Dependability

Efficiency

4 💹 Scale structure of the UEQ

5 ⏰ By Period

We used an ordinal logistic regression model to account for the ordered nature of the response scale. This allowed us to estimate the likelihood of each response level across time periods or experience in VHF response and visualize these shifts using the predicted probability plot.

5.1 📈 Attractiveness

Significance of Period Effect

Observed Response

Annoying – Enjoyable

Good – Bad

Unlikable – Pleasing

Unpleasant – Pleasant

Attractive – Unattractive

Friendly – Unfriendly

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.

Annoying – Enjoyable

Good – Bad

Unlikable – Pleasing

Unpleasant – Pleasant

Attractive – Unattractive

Friendly – Unfriendly

5.2 📈 Novelty

Significance of Period Effect

Observed Response

Creative – Dull

Inventive – Conventional

Usual – Leading edge

Conservative – Innovative

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.

Creative – Dull

Inventive – Conventional

Usual – Leading edge

Conservative – Innovative

5.3 📈 Perspicuity

Significance of Period Effect

Observed Response

Not understandable – Understandable

Easy to learn – Difficult to learn

Complicated – Easy

Clear – Confusing

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.

Not understandable – Understandable

Easy to learn – Difficult to learn

Complicated – Easy

Clear – Confusing

5.4 📈 Stimulation

Significance of Period Effect

Observed Response

Valuable – Inferior

Boring – Exciting

Not interesting – Interesting

Motivating – Demotivating

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.

Valuable – Inferior

Boring – Exciting

Not interesting – Interesting

Motivating – Demotivating

5.5 📈 Dependability

Significance of Period Effect

Observed Response

Unpredictable – Predictable

Obstructive – Supportive

Secure – Not secure

Meets expectations – Does not meet expectations

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.

Unpredictable – Predictable

Obstructive – Supportive

Secure – Not secure

Meets expectations – Does not meet expectations

5.6 📈 Efficiency

Significance of Period Effect

Observed Response

Fast – Slow

Inefficiant – Efficient

Impractical – Practical

Organized – Cluttered

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.

Fast – Slow

Inefficiant – Efficient

Impractical – Practical

Organized – Cluttered

6 ⚠️ Inconsistencies

Critical length

Based on the number of identical answers (in the original answer scale in “Data”). If a participant for example crosses for all items the middle category (“4”) this can hardly be accepted as a serious response. Such answers are most likely result from an attempt to quickly finish the survey without much thinking. An analysis of several UEQ data sets suggests a heuristic to remove answers that show for more than 15 items the same response. This heuristic is implemented in the Column “Critical Length”. See the UEQ handbook for details.

Here, we have 4 participants who answered the same answer ≥ 16

Scales with inconsitent answers

To detect such suspicious answers, a simple heuristic is used. All items in a scale should measure a similar UX quality aspect. The idea behind the heuristic is to check how much the best and worst evaluation of an item in a scale differs. If there is a big difference (>3) this is seen as an indicator for a problematic data pattern. Of course such situations can also result from random response errors or a misunderstanding of an item. Thus, it makes no sense to delete a response if this occurs just for a single scale. But if this is true for 2 or 3 scales this is of course a clear hint that the response is somehow suspicious.

Here, we have 22 participants who had large difference between responses (>3) across 2 or 3 scales (highlighted in red below)

7 👨🏽‍⚕️Experience in VHF Response

7.1 📈 Attractiveness

Significance of the Experience in VHF Response

Observed Response

Annoying – Enjoyable

Good – Bad

Unlikable – Pleasing

Unpleasant – Pleasant

Attractive – Unattractive

Friendly – Unfriendly

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.

Annoying – Enjoyable

Good – Bad

Unlikable – Pleasing

Unpleasant – Pleasant

Attractive – Unattractive

Friendly – Unfriendly

7.2 📈 Novelty

Significance of the Experience in VHF Response

Observed Response

Creative – Dull

Inventive – Conventional

Usual – Leading edge

Conservative – Innovative

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.

Creative – Dull

Inventive – Conventional

Usual – Leading edge

Conservative – Innovative

7.3 📈 Perspicuity

Significance of the Experience in VHF Response

Observed Response

Not understandable – Understandable

Easy to learn – Difficult to learn

Complicated – Easy

Clear – Confusing

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.

Not understandable – Understandable

Easy to learn – Difficult to learn

Complicated – Easy

Clear – Confusing

7.4 📈 Stimulation

Significance of the Experience in VHF Response

Observed Response

Valuable – Inferior

Boring – Exciting

Not interesting – Interesting

Motivating – Demotivating

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.

Valuable – Inferior

Boring – Exciting

Not interesting – Interesting

Motivating – Demotivating

7.5 📈 Dependability

Significance of the Experience in VHF Response

Observed Response

Unpredictable – Predictable

Obstructive – Supportive

Secure – Not secure

Meets expectations – Does not meet expectations

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.

Unpredictable – Predictable

Obstructive – Supportive

Secure – Not secure

Meets expectations – Does not meet expectations

7.6 📈 Efficiency

Significance of the Experience in VHF Response

Observed Response

Fast – Slow

Inefficiant – Efficient

Impractical – Practical

Organized – Cluttered

Predited Probabilities

The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.

Fast – Slow

Inefficiant – Efficient

Impractical – Practical

Organized – Cluttered